mlsanityData to 14 Oct 2026

Super-Resolution on Rotationally Scanned Photoacoustic Microscopy Images Incorporating Scanning Prior

arXiv 2312.07226 ↗First posted 12 Dec 2023eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Photoacoustic Microscopy (PAM) images integrating the advantages of optical contrast and acoustic resolution have been widely used in brain studies. However, there exists a trade-off between scanning speed and image resolution. Compared with traditional raster scanning, rotational scanning provides good opportunities for fast PAM imaging by optimizing the scanning mechanism. Recently, there is a trend to incorporate deep learning into the scanning process to further increase the scanning speed.Yet, most such attempts are performed for raster scanning while those for rotational scanning are relatively rare. In this study, we propose a novel and well-performing super-resolution framework for rotational scanning-based PAM imaging. To eliminate adjacent rows' displacements due to subject motion or high-frequency scanning distortion,we introduce a registration module across odd and even rows in the preprocessing and incorporate displacement degradation in the training. Besides, gradient-based patch selection is proposed to increase the probability of blood vessel patches being selected for training. A Transformer-based network with a global receptive field is applied for better performance. Experimental results on both synthetic and real datasets demonstrate the effectiveness and generalizability of our proposed framework for rotationally scanned PAM images'super-resolution, both quantitatively and qualitatively. Code is available at https://github.com/11710615/PAMSR.git.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 55 references that are in the map. First posted on arXiv 12 Dec 2023.

Builds on

21 of 55 references.

References · 21

  1. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  3. 2022Generative Adversarial Networkscited by 5,627
  4. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  5. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
  6. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,627
  7. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
  8. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,264
  9. 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 970
  10. 2021Uformer: A General U-Shaped Transformer for Image Restorationcited by 866
  11. 2018Residual Dense Network for Image Super-Resolutioncited by 799
  12. 2021Designing a Practical Degradation Model for Deep Blind Image Super-Resolutioncited by 529
9 more
  1. 2022Activating More Pixels in Image Super-Resolution Transformercited by 438
  2. 2020Single Image Super-Resolution via a Holistic Attention Networkcited by 265
  3. 2023Dual Aggregation Transformer for Image Super-Resolutioncited by 124
  4. 2020Structure-Preserving Super Resolution with Gradient Guidancecited by 116
  5. 2022Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolutioncited by 114
  6. 2021Deep Burst Super-Resolutioncited by 83
  7. 2021Burst Image Restoration and Enhancementcited by 64
  8. 2016Deep Edge Guided Recurrent Residual Learning for Image Super-Resolutioncited by 22
  9. 2020Reconstructing undersampled photoacoustic microscopy images using deep learningcited by 7

Built on by

Not cited yet.